Comparer des méthodes

	Cross-Classified Multilevel Models in Education	Modélisation de la valeur ajoutée
Domaine	Education	Psychométrie
Famille	Regression model	Latent structure
Année d'origine	1993	1998
Auteur d'origine	Multilevel modeling community (Raudenbush; Goldstein; Rasbash & Browne)	William Sanders, Sandra Horn
Type	Multilevel model with units cross-classified by two or more non-nested groupings	Longitudinal student achievement modeling
Source fondatrice	Goldstein, H. (2011). Multilevel Statistical Models (4th ed.). Wiley. ISBN: 9780470748657	Kane, T. J., Rockoff, J. E., & Staiger, D. O. (2008). What does certification tell us about teacher effectiveness? Evidence from New York City. Economics of Education Review, 27(6), 615-631.
Alias	Cross-Classified Random Effects Models, CCREM, Cross-Classified Multilevel Modeling, Multiple Membership Cross-Classified Models	VAM
Apparentées	4	4
Résumé	Cross-classified multilevel models extend hierarchical linear modeling to situations where units belong to two or more groupings that do not nest neatly inside one another. In education, students are often classified by both school and neighborhood, or by primary and secondary school across time — classifications that cut across each other rather than form a clean hierarchy. These models assign a random effect to each classification simultaneously, partitioning variance among them and yielding correct inferences where a purely nested model would be misspecified.	Value-Added Modeling (VAM) is a method for assessing the contribution of schools or teachers to student achievement growth, developed by Sanders and Horn (1998). VAM isolates the effect of a teacher or school by comparing student gains (value added) while controlling for prior achievement and student characteristics.